from pyhealth.data import Patient, Visit
[docs]def mortality_prediction_mimic3_fn(patient: Patient):
"""Processes a single patient for the mortality prediction task.
Mortality prediction aims at predicting whether the patient will decease in the
next hospital visit based on the clinical information from current visit
(e.g., conditions and procedures).
Args:
patient: a Patient object
Returns:
samples: a list of samples, each sample is a dict with patient_id,
visit_id, and other task-specific attributes as key
Note that we define the task as a binary classification task.
Examples:
>>> from pyhealth.datasets import MIMIC3Dataset
>>> mimic3_base = MIMIC3Dataset(
... root="/srv/local/data/physionet.org/files/mimiciii/1.4",
... tables=["DIAGNOSES_ICD", "PROCEDURES_ICD", "PRESCRIPTIONS"],
... code_mapping={"ICD9CM": "CCSCM"},
... )
>>> from pyhealth.tasks import mortality_prediction_mimic3_fn
>>> mimic3_sample = mimic3_base.set_task(mortality_prediction_mimic3_fn)
>>> mimic3_sample.samples[0]
[{'visit_id': '130744', 'patient_id': '103', 'conditions': [['42', '109', '19', '122', '98', '663', '58', '51']], 'procedures': [['1']], 'label': 0}]
"""
samples = []
# we will drop the last visit
for i in range(len(patient) - 1):
visit: Visit = patient[i]
next_visit: Visit = patient[i + 1]
if next_visit.discharge_status not in [0, 1]:
mortality_label = 0
else:
mortality_label = int(next_visit.discharge_status)
conditions = visit.get_code_list(table="DIAGNOSES_ICD")
procedures = visit.get_code_list(table="PROCEDURES_ICD")
drugs = visit.get_code_list(table="PRESCRIPTIONS")
# exclude: visits without condition, procedure, and drug code
if len(conditions) * len(procedures) * len(drugs) == 0:
continue
# TODO: should also exclude visit with age < 18
samples.append(
{
"visit_id": visit.visit_id,
"patient_id": patient.patient_id,
"conditions": [conditions],
"procedures": [procedures],
"drugs": [drugs],
"label": mortality_label,
}
)
# no cohort selection
return samples
[docs]def mortality_prediction_mimic4_fn(patient: Patient):
"""Processes a single patient for the mortality prediction task.
Mortality prediction aims at predicting whether the patient will decease in the
next hospital visit based on the clinical information from current visit
(e.g., conditions and procedures).
Args:
patient: a Patient object
Returns:
samples: a list of samples, each sample is a dict with patient_id,
visit_id, and other task-specific attributes as key
Note that we define the task as a binary classification task.
Examples:
>>> from pyhealth.datasets import MIMIC4Dataset
>>> mimic4_base = MIMIC4Dataset(
... root="/srv/local/data/physionet.org/files/mimiciv/2.0/hosp",
... tables=["diagnoses_icd", "procedures_icd"],
... code_mapping={"ICD10PROC": "CCSPROC"},
... )
>>> from pyhealth.tasks import mortality_prediction_mimic4_fn
>>> mimic4_sample = mimic4_base.set_task(mortality_prediction_mimic4_fn)
>>> mimic4_sample.samples[0]
[{'visit_id': '130744', 'patient_id': '103', 'conditions': [['42', '109', '19', '122', '98', '663', '58', '51']], 'procedures': [['1']], 'label': 1}]
"""
samples = []
# we will drop the last visit
for i in range(len(patient) - 1):
visit: Visit = patient[i]
next_visit: Visit = patient[i + 1]
if next_visit.discharge_status not in [0, 1]:
mortality_label = 0
else:
mortality_label = int(next_visit.discharge_status)
conditions = visit.get_code_list(table="diagnoses_icd")
procedures = visit.get_code_list(table="procedures_icd")
drugs = visit.get_code_list(table="prescriptions")
# exclude: visits without condition, procedure, or drug code
if len(conditions) * len(procedures) * len(drugs) == 0:
continue
# TODO: should also exclude visit with age < 18
samples.append(
{
"visit_id": visit.visit_id,
"patient_id": patient.patient_id,
"conditions": [conditions],
"procedures": [procedures],
"drugs": [drugs],
"label": mortality_label,
}
)
# no cohort selection
return samples
[docs]def mortality_prediction_eicu_fn(patient: Patient):
"""Processes a single patient for the mortality prediction task.
Mortality prediction aims at predicting whether the patient will decease in the
next hospital visit based on the clinical information from current visit
(e.g., conditions and procedures).
Args:
patient: a Patient object
Returns:
samples: a list of samples, each sample is a dict with patient_id,
visit_id, and other task-specific attributes as key
Note that we define the task as a binary classification task.
Examples:
>>> from pyhealth.datasets import eICUDataset
>>> eicu_base = eICUDataset(
... root="/srv/local/data/physionet.org/files/eicu-crd/2.0",
... tables=["diagnosis", "medication"],
... code_mapping={},
... dev=True
... )
>>> from pyhealth.tasks import mortality_prediction_eicu_fn
>>> eicu_sample = eicu_base.set_task(mortality_prediction_eicu_fn)
>>> eicu_sample.samples[0]
[{'visit_id': '130744', 'patient_id': '103', 'conditions': [['42', '109', '98', '663', '58', '51']], 'procedures': [['1']], 'label': 0}]
"""
samples = []
# we will drop the last visit
for i in range(len(patient) - 1):
visit: Visit = patient[i]
next_visit: Visit = patient[i + 1]
if next_visit.discharge_status not in ["Alive", "Expired"]:
mortality_label = 0
else:
mortality_label = 0 if next_visit.discharge_status == "Alive" else 1
conditions = visit.get_code_list(table="diagnosis")
procedures = visit.get_code_list(table="physicalExam")
drugs = visit.get_code_list(table="medication")
# exclude: visits without condition, procedure, or drug code
if len(conditions) * len(procedures) * len(drugs) == 0:
continue
# TODO: should also exclude visit with age < 18
samples.append(
{
"visit_id": visit.visit_id,
"patient_id": patient.patient_id,
"conditions": [conditions],
"procedures": [procedures],
"drugs": [drugs],
"label": mortality_label,
}
)
# no cohort selection
return samples
[docs]def mortality_prediction_omop_fn(patient: Patient):
"""Processes a single patient for the mortality prediction task.
Mortality prediction aims at predicting whether the patient will decease in the
next hospital visit based on the clinical information from current visit
(e.g., conditions and procedures).
Args:
patient: a Patient object
Returns:
samples: a list of samples, each sample is a dict with patient_id,
visit_id, and other task-specific attributes as key
Note that we define the task as a binary classification task.
Examples:
>>> from pyhealth.datasets import OMOPDataset
>>> omop_base = OMOPDataset(
... root="https://storage.googleapis.com/pyhealth/synpuf1k_omop_cdm_5.2.2",
... tables=["condition_occurrence", "procedure_occurrence"],
... code_mapping={},
... )
>>> from pyhealth.tasks import mortality_prediction_omop_fn
>>> omop_sample = omop_base.set_task(mortality_prediction_eicu_fn)
>>> omop_sample.samples[0]
[{'visit_id': '130744', 'patient_id': '103', 'conditions': [['42', '109', '98', '663', '58', '51']], 'procedures': [['1']], 'label': 1}]
"""
samples = []
# we will drop the last visit
for i in range(len(patient) - 1):
visit: Visit = patient[i]
next_visit: Visit = patient[i + 1]
mortality_label = int(next_visit.discharge_status)
conditions = visit.get_code_list(table="condition_occurrence")
procedures = visit.get_code_list(table="procedure_occurrence")
drugs = visit.get_code_list(table="drug_exposure")
# labs = visit.get_code_list(table="measurement")
# exclude: visits without condition, procedure, or drug code
if len(conditions) * len(procedures) * len(drugs) == 0:
continue
# TODO: should also exclude visit with age < 18
samples.append(
{
"visit_id": visit.visit_id,
"patient_id": patient.patient_id,
"conditions": [conditions],
"procedures": [procedures],
"drugs": [drugs],
"label": mortality_label,
}
)
# no cohort selection
return samples
if __name__ == "__main__":
from pyhealth.datasets import MIMIC3Dataset
base_dataset = MIMIC3Dataset(
root="/srv/local/data/physionet.org/files/mimiciii/1.4",
tables=["DIAGNOSES_ICD", "PROCEDURES_ICD", "PRESCRIPTIONS"],
dev=True,
code_mapping={"ICD9CM": "CCSCM", "NDC": "ATC"},
refresh_cache=False,
)
sample_dataset = base_dataset.set_task(task_fn=mortality_prediction_mimic3_fn)
sample_dataset.stat()
print(sample_dataset.available_keys)
from pyhealth.datasets import MIMIC4Dataset
base_dataset = MIMIC4Dataset(
root="/srv/local/data/physionet.org/files/mimiciv/2.0/hosp",
tables=["diagnoses_icd", "procedures_icd", "prescriptions"],
dev=True,
code_mapping={"NDC": "ATC"},
refresh_cache=False,
)
sample_dataset = base_dataset.set_task(task_fn=mortality_prediction_mimic4_fn)
sample_dataset.stat()
print(sample_dataset.available_keys)
from pyhealth.datasets import eICUDataset
base_dataset = eICUDataset(
root="/srv/local/data/physionet.org/files/eicu-crd/2.0",
tables=["diagnosis", "medication", "physicalExam"],
dev=True,
refresh_cache=False,
)
sample_dataset = base_dataset.set_task(task_fn=mortality_prediction_eicu_fn)
sample_dataset.stat()
print(sample_dataset.available_keys)
from pyhealth.datasets import OMOPDataset
base_dataset = OMOPDataset(
root="/srv/local/data/zw12/pyhealth/raw_data/synpuf1k_omop_cdm_5.2.2",
tables=["condition_occurrence", "procedure_occurrence", "drug_exposure"],
dev=True,
refresh_cache=False,
)
sample_dataset = base_dataset.set_task(task_fn=mortality_prediction_omop_fn)
sample_dataset.stat()
print(sample_dataset.available_keys)